# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.utils.deepspeed import DummyOptim, DummyScheduler MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 def get_dataloaders(accelerator: Accelerator, batch_size: int = 16, model_name: str = "bert-base-cased"): """ Creates a set of `DataLoader`s for the `glue` dataset. Args: accelerator (`Accelerator`): An `Accelerator` object batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. model_name (`str`, *optional*): """ tokenizer = AutoTokenizer.from_pretrained(model_name) datasets = load_dataset("glue", "mrpc") def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=False ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader( tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size ) eval_dataloader = DataLoader( tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) return train_dataloader, eval_dataloader def training_function(config, args): # Initialize accelerator accelerator = Accelerator() # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) model_name = args.model_name_or_path set_seed(seed) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size, model_name) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=True) # Instantiate optimizer optimizer_cls = ( AdamW if accelerator.state.deepspeed_plugin is None or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) optimizer = optimizer_cls(params=model.parameters(), lr=lr) if accelerator.state.deepspeed_plugin is not None: gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[ "gradient_accumulation_steps" ] else: gradient_accumulation_steps = 1 max_training_steps = (len(train_dataloader) * num_epochs) // gradient_accumulation_steps # Instantiate scheduler if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=0, num_training_steps=max_training_steps, ) else: lr_scheduler = DummyScheduler(optimizer, total_num_steps=max_training_steps, warmup_num_steps=0) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to keep track of how many total steps we have iterated over overall_step = 0 # We also need to keep track of the stating epoch so files are named properly starting_epoch = 0 # Now we train the model metric = evaluate.load("glue", "mrpc") best_performance = 0 performance_metric = {} for epoch in range(starting_epoch, num_epochs): model.train() for step, batch in enumerate(train_dataloader): outputs = model(**batch) loss = outputs.loss loss = loss / gradient_accumulation_steps accelerator.backward(loss) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() overall_step += 1 model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) # It is slightly faster to call this once, than multiple times predictions, references = accelerator.gather( (predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.use_distributed: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(f"epoch {epoch}:", eval_metric) performance_metric[f"epoch-{epoch}"] = eval_metric["accuracy"] if best_performance < eval_metric["accuracy"]: best_performance = eval_metric["accuracy"] if args.performance_lower_bound is not None: assert ( args.performance_lower_bound <= best_performance ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}" accelerator.wait_for_everyone() if accelerator.is_main_process: with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(performance_metric, f) def main(): parser = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.") parser.add_argument( "--model_name_or_path", type=str, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--output_dir", type=str, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", ) parser.add_argument( "--performance_lower_bound", type=float, default=None, help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", ) parser.add_argument( "--num_epochs", type=int, default=3, help="Number of train epochs.", ) args = parser.parse_args() config = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16} training_function(config, args) if __name__ == "__main__": main()